Markov chain Monte Carlo methods for state-space models with point process observations


Yuan, Ke, Girolami, Mark and Niranjan, Mahesan (2012) Markov chain Monte Carlo methods for state-space models with point process observations. Neural Computation, 24, (6), 1462-1486. (doi:10.1162/NECO_a_00281). (PMID:22364499).

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Description/Abstract

This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers the best performance. We further compared such a method with a previously tested variational Bayes method on two experimental data sets. Results indicate similar performance on the large data sets and superior performance on small ones. The work offers an extensive suite of MCMC algorithms evaluated on an important class of models for physiological signal analysis.

Item Type: Article
ISSNs: 0899-7667 (print)
1530-888X (electronic)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QP Physiology
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Comms, Signal Processing & Control
ePrint ID: 337632
Date Deposited: 01 May 2012 08:02
Last Modified: 27 Mar 2014 20:21
Further Information:Google Scholar
ISI Citation Count:0
URI: http://eprints.soton.ac.uk/id/eprint/337632

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